Igor T. Podolak - researchr alias

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2024

2023

Zero time waste in pre-trained early exit neural networksBartosz Wójcik, Marcin Przewiezlikowski, Filip Szatkowski, Maciej Wolczyk, Klaudia Balazy, Bartlomiej Krzepkowski, Igor T. Podolak, Jacek Tabor, Marek Smieja, Tomasz Trzcinski. NN, 168:580-601, November 2023. [doi]

2022

Batch Size Reconstruction-Distribution Trade-Off In Kernel Based Generative AutoencodersSzymon Knop, Przemyslaw Spurek, Marcin Mazur, Jacek Tabor, Igor T. Podolak. icip 2022: 3728-3732 [doi]

Generative models with kernel distance in data spaceSzymon Knop, Marcin Mazur, Przemyslaw Spurek, Jacek Tabor, Igor T. Podolak. ijon, 487:119-129, 2022. [doi]

2021

2D SIFt: a matrix of ligand-receptor interactionsStefan Mordalski, Agnieszka Wojtuch, Igor T. Podolak, Rafal Kurczab, Andrzej J. Bojarski. jcheminf, 13(1):66, 2021. [doi]

Zero Time Waste: Recycling Predictions in Early Exit Neural NetworksMaciej Wolczyk, Bartosz Wójcik, Klaudia Balazy, Igor T. Podolak, Jacek Tabor, Marek Smieja, Tomasz Trzcinski. nips 2021: 2516-2528 [doi]

2020

Cramer-Wold Auto-EncoderSzymon Knop, Przemyslaw Spurek, Jacek Tabor, Igor T. Podolak, Marcin Mazur, Stanislaw Jastrzebski. jmlr, 21, 2020. [doi]

2019

Distribution-Interpolation Trade off in Generative ModelsDamian Lesniak, Igor Sieradzki, Igor T. Podolak. iclr 2019: [doi]

Variational auto-encoders for generating feature-preserving automataIgor T. Podolak, Adam Roman, Arkadiusz Czekajski, Damian Lesniak, Bartosz Zielinski 0001. ncma 2019: 171-185

2018

A machine learning approach to synchronization of automataIgor T. Podolak, Adam Roman, Marek Szykula, Bartosz Zielinski 0001. eswa, 97:357-371, 2018. [doi]

2014

Adaptive Active Learning as a Multi-armed Bandit ProblemWojciech M. Czarnecki, Igor T. Podolak. ecai 2014: 989-990 [doi]

2013

Machine Learning with Known Input Data Uncertainty MeasureWojciech M. Czarnecki, Igor T. Podolak. cisim 2013: 379-388 [doi]

Density Invariant Detection of Osteoporosis Using Growing Neural GasIgor T. Podolak, Stanislaw K. Jastrzebski. cores 2013: 629-638 [doi]

Theoretical Foundations and Experimental Results for a Hierarchical Classifier with Overlapping ClustersIgor T. Podolak, Adam Roman. ci, 29(2):357-388, 2013. [doi]

2012

Application of Hierarchical Classifier to Minimal Synchronizing Word ProblemIgor T. Podolak, Adam Roman, Dariusz Jedrzejczyk. icaisc 2012: 421-429 [doi]

2011

2009

A Hierarchical Classifier with Growing Neural Gas ClusteringIgor T. Podolak, Kamil Bartocha. icannga 2009: 283-292 [doi]

A New Notion of Weakness in Classification TheoryIgor T. Podolak, Adam Roman. In Marek Kurzynski, Michal Wozniak, editors, Computer Recognition Systems 3. Volume 57 of Advances in Intelligent and Soft Computing, pages 239-245, Springer, 2009. [doi]

2008

Algorithm for Intelligent Prediction of Requests in Business SystemsPiotr Kalita, Igor T. Podolak, Adam Roman, Bartosz Bierkowski. sofsem 2008: 696-707 [doi]

Hierarchical classifier with overlapping class groupsIgor T. Podolak. eswa, 34(1):673-682, 2008. [doi]

2007

Hierarchical Rules for a Hierarchical ClassifierIgor T. Podolak. icannga 2007: 749-757 [doi]

2005

Hierarchical ClassifierIgor T. Podolak, Slawomir Biel, Marcin Bobrowski. ppam 2006: 591-598 [doi]

2002

Facial Component Extraction and Face Recognition with Support Vector MachinesDihua Xi, Igor T. Podolak, Seong-Whan Lee. fgr 2002: 83-88 [doi]

2000

A Hybrid Neural System for Phonematic TransformationIgor T. Podolak, Seong-Whan Lee, Andrzej Bielecki, Elzbieta Majkut. icpr 2000: 2957-2960 [doi]

1998

Functional graph model of a neural networkIgor T. Podolak. tsmc, 28(6):876-881, 1998.

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